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Functions provide only the ability to return a single thing from a stored routine. Several stored procedure dialects provide a method for returning more than one value (or other thing), by passing the values back to the calling routine through output parameters. The output parameters are listed in the stored procedure s parameter list, just like the input parameters seen in the previous examples. However, instead of being used to pass data values into the stored procedure when it is called, the output parameters are used to pass data back out of the stored procedure to the calling procedure. Figure 20-9 shows a PL/SQL stored procedure to retrieve the name of a customer, his or her salesperson, and the sales office to which the customer is assigned, given a supplied customer number. The procedure has four parameters. The first one, CNUM, is an input parameter and supplies the requested customer number. The other three parameters are output parameters, used to pass the retrieved data values back to the calling procedure.

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seems to be a coordinated approach to data transformation for distributed marts, rather than a return to huge centralized warehouses. Data warehousing, and more recently data marts, have grown to prominence in many different industries. They are most widely (and aggressively) used in industries where better information about business trends can be used to make decisions that save or generate large amounts of money. For example: I High-volume manufacturing. Analysis of customer purchase trends, seasonality, and so on can help the company plan its production and lower its inventory levels, saving money for other purposes. I Packaged goods. Analysis of promotions (coupons, advertising campaigns, direct mail, and so on) and the response of consumers with different demographics can help to determine the most effective way to reach prospective customers, saving millions of dollars in advertising and promotion costs. I Telecommunications. Analysis of customer calling patterns can help to create more attractive pricing and promotional plans, perhaps attracting new customers from a competitor. I Airlines. Analysis of customer travel patterns is critical to yield management, the process of setting airfares and associated restrictions on available airline seats to maximize profitability. I Financial services. Analysis of customer credit factors and comparing them to historical customer profiles can help to make better decisions about which customers are creditworthy.

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/* Return total order amount for a customer */ create function get_tot_ords(c_num in integer) returning numeric(16,2) /* Declare one local variable to hold the total */ define tot_ord numeric(16,2); begin /* Simple single-row query to get total */ select sum(amount) into tot_ord from orders where cust = c_num; /* Return the retrieved value as fcn value */ return tot_ord; end function;

The structure (schema) of a warehouse database is typically designed to make the information easy to analyze, since that is the major focus of its use. The structure must make it easy to slice and dice the data along various dimensions. For example, one day a business analyst may want to look at sales by product category by region, to compare the performance of different products in different areas of the country. The next day, the same analyst may want to look at sales trends over time by region, to see which regions are growing and which are not. The structure of the database must lend itself to this type of analysis along several different dimensions.

FIGURE 20-8

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In most cases, the data stored in a warehouse can be accurately modeled as an N-dimensional cube (N-cube) of historical business facts. A simple three-dimensional cube of sales data is shown in Figure 21-2 to illustrate the structure. The fact in each cell of the cube is a dollar sales amount. Along one edge of the cube, one of the dimensions is the month during which the sales took place. Another dimension is the region where the sales occurred. The third dimension is the type of product that was sold. Each cell in the cube represents the sales for one combination of these three dimensions. The

Ends the current transaction Aborts the current transaction Defines data access characteristics of the current transaction

Part VI:

21:

/* Get customer name, salesrep, and office */ create procedure get_cust_info(c_num in number, c_name out varchar, r_name out varchar, c_offc out varchar) as begin /* Simple single-row query to get info */ select company, name, city into c_name, r_name, c_offc from customers, salesreps, offices where cust_num = c_num and empl_num = cust_rep and office = rep_office; end;

Figure 21-2.

In this simple example, the SELECT...INTO form of the query places the returned variables directly into the output parameters. In a more complex stored procedure, the returned values might be calculated and placed into the output parameters with a PL/SQL assignment statement. When a stored procedure with output parameters is called, the value passed for each output parameter must be an acceptable target that can receive the returned data value. The target may be a local variable, for example, or a parameter of a higher-level procedure that is calling a lower-level procedure to do some work for it. Here is an Oracle PL/SQL anonymous (unnamed) block that makes an appropriate call to the GET_CUST_INFO procedure in Figure 20-9:

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